Electric power steering apparatus

ABSTRACT

The present invention is the electric power steering apparatus that a motor to assist-control a steering system of a vehicle is connected to a steering shaft via a reduction mechanism, and comprises a first angle sensor to detect a steering shaft angle of the steering shaft and a second angle sensor to detect a motor shaft angle of the motor, comprising: a function that obtains compensation value maps by iteratively learning characteristics of nonlinear elements including the reduction mechanism based on an actual measuring angle of the first angle sensor, an actual measuring angle of the second angle sensor, a motor torque and a motor angular speed, and estimates the steering shaft angle and the motor shaft angle by using the compensation value maps.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a National Stage of International Application No.PCT/JP2016/070124 filed Jul. 7, 2016, claiming priority based onJapanese Patent Application Nos. 2015-136479 filed Jul. 7, 2015,2015-200388 filed Oct. 8, 2015 and 2015-210753 filed Oct. 27, 2015, thecontents of all of which are incorporated herein by reference in theirentirety.

TECHNICAL FIELD

The present invention relates to an electric power steering apparatusthat driving-controls a motor by means of a current command value andassist-controls a steering system of a vehicle by driving-controllingthe motor, and in particular to the electric power steering apparatusthat comprises an angle sensor disposed at a steering shaft (a pinionside) and an angle sensor disposed at a motor shaft, estimates a motorshaft angle and a steering shaft with a high accuracy by learningnonlinear elements of a mechanism system including a reduction mechanismsystem and the steering system as needed, judges a failure (including anabnormality) in comparison with an actual measuring value and anestimating value, and in a case that one angle sensor is failed, ispossible to back up one angle sensor by utilizing a detection angle ofthe other angle sensor.

The present invention also relates to the electric power steeringapparatus that divides the nonlinear elements into a staticcharacteristic (an angle error), a dynamic characteristic (an angleerror) and a delay (a phase error), learns a single element or acombination thereof appropriately, is able to judge the failure(including the abnormality) of the steering system or the sensor systembased on a result of the learning, and is able to deal with wideoperations in a range of a steering holding of a handle or a slowsteering to a high speed steering.

BACKGROUND ART

An electric power steering apparatus (EPS) which provides a steeringmechanism of a vehicle with a steering assist torque (an assist torque)by means of a rotational torque of a motor, applies a driving force ofthe motor as a steering assist torque to a steering shaft or a rackshaft by means of a transmission mechanism such as gears or a beltthrough a reduction mechanism, and assist-controls the steeringmechanism of the vehicle. In order to accurately generate the assisttorque, such a conventional electric power steering apparatus performs afeed-back control of a motor current. The feed-back control adjusts avoltage supplied to the motor so that a difference between a steeringassist command value (a current command value) and a detected motorcurrent value becomes small, and the adjustment of the voltage suppliedto the motor is generally performed by an adjustment of duty commandvalues of a pulse width modulation (PWM) control.

A general configuration of the conventional electric power steeringapparatus will be described with reference to FIG. 1. As shown in FIG.1, a steering shaft (a column shaft or a handle shaft) 2 connected to ahandle (a steering wheel) 1 is connected to steered wheels 8L and 8Rthrough reduction gears (a worm gear and a worm) 3, universal joints 4 aand 4 b, a rack-and-pinion mechanism 5, and tie rods 6 a and 6 b,further via hub units 7 a and 7 b. In addition, the torsion bar isinterposed within the steering shaft 2, the steering shaft 2 is providedwith a steering angle sensor 14 for detecting a steering angle θ of thehandle 1 by means of a torsional angle of the torsion bar and a torquesensor 10 for detecting a steering torque Th, and a motor 20 forassisting the steering torque of the handle 1 is connected to the columnshaft 2 through the reduction gears 3. The electric power is supplied toa control unit (ECU) 30 for controlling the electric power steeringapparatus from a battery 13, and an ignition key signal is inputted intothe control unit 30 through an ignition key 11. The control unit 30calculates a current command value of an assist control on the basis ofthe steering torque Th detected by the torque sensor 10 and a vehiclespeed Vel detected by a vehicle speed sensor 12, and controls a currentsupplied to the motor 20 by means of a voltage control command valueVref obtained by performing compensation or the like to the currentcommand value. It is possible to receive the vehicle speed Vel from acontroller area network (CAN) or the like.

A controller area network (CAN) 40 to send/receive various informationand signals on the vehicle is connected to the control unit 30, and itis also possible to receive the vehicle speed Vel from the CAN 40.Further, a Non-CAN 41 is also possible to connect to the control unit30, and the Non-CAN 41 sends and receives a communication,analogue/digital signals, electric wave or the like except for the CAN40.

The control unit 30 mainly comprises a CPU (Central Processing Unit)(including an MPU (Micro Processor Unit) and an MCU (Micro ControllerUnit)), and general functions performed by programs within the CPU are,for example, shown in FIG. 2.

The control unit 30 will be described with reference to FIG. 2. As shownin FIG. 2, the steering torque Th detected by the torque sensor 10 andthe vehicle speed Vel detected by the vehicle speed sensor 12 (or fromthe CAN 40) are inputted into a current command value calculatingsection 31 which calculates the current command value Iref1. The currentcommand value calculating section 31 calculates the current commandvalue Iref1, based on the steering torque Th and the vehicle speed Velwith reference to an assist map or the like, which is a control targetvalue of a current supplied to the motor 20. The calculated currentcommand value Iref1 is inputted into a current limiting section 33 viaan adding section 32A, and the current command value Irefm whose maximumcurrent is limited is inputted into a subtracting section 32B. A currentdeviation I (=Irefm−Im) between the current command value Irefm and amotor current value Im which is fed-back is calculated at thesubtracting section 32B, and the current deviation I is inputted into acurrent control section 35 which performs aproportional-integral-control (PI-control) and the like for improving acurrent characteristic of the steering operation. The voltage controlcommand value Vref that the characteristic is improved at the currentcontrol section 35, is inputted into a PWM-control section 36, and themotor 20 is PWM-driven through an inverter 37 serving as a drivingsection. The motor current value Im of the motor 20 is detected by amotor current detector 38 and is fed-back to the subtracting section32B. The inverter 37 is constituted by a bridge circuit of field-effecttransistors (FETs) as semiconductor switching devices.

A rotational sensor 21 such as a resolver is connected to the motor 20and a motor rotational angle θ is outputted.

A compensation signal CM from a compensation signal generating section34 is added at the adding section 32A. A characteristic compensation ofthe steering system is performed by adding the compensation signal CM,and a convergence, an inertia characteristic and the like are improved.The compensation signal generating section 34 adds a self-aligningtorque (SAT) 343 to an inertia 342 at an adding section 344. The addingresult is further added with a convergence 341 at an adding section 345.The adding result at the adding section 345 is treated as thecompensation signal CM.

In the electric power steering apparatus which is described above,recently, the torque sensors and the angle sensors are sometime equippedwith multiplexing due to requirements of a reliability improvement, afunctional redundancy and so on. However, because the requirement of acost reduction is also existed, it is not easy to simply multiplex thesensors. Therefore, by utilizing at the maximum the limited sensorswhich are currently mounted on the vehicle, a method to monitor anddiagnose the sensors each other is preferred. The steering shaft of theelectric power steering apparatus is connected to the motor shaft viathe reduction mechanism such as the worm gear and the worm.

Further, in a case of multiplexing the angle sensors, that is, in a casethat the dual-system angle sensors are equipped with the steering shaftand the motor shaft, when one system is failed, it is considered thatthe other system backs up the failed system. However, in general, sincethe mechanism system including the reduction mechanism and the steeringsystem have nonlinear elements such as friction, backlash, an elasticcoupling of the motor output shaft, preload to gear surfaces by means ofa worm wheel and the worm, and lubricating grease of the reductionmechanism section, an angle of the steering shaft is different from thatof the motor shaft and therefore an angle error occurs. In thisconnection, when one of the angle sensors is failed, the other of theangle sensors cannot immediately back up (substitution in the failure)the failed angle sensor.

As a prior art, WO 04/022414 (Patent Document 1) discloses a method formeasuring a torque for a vehicle having an electromechanical steeringsystem, and the disclosed method is considered as a torque sensor forbackup. An overall configuration is an electromechanical steering systemcomprising an input shaft section and an output shaft section beingconnected to a driving steering mechanism, and a steering means having aservo moto being connected via a torsion bar. Although the configurationis the electromechanical steering apparatus (a digital circuit or ananalog circuit) which performs torque detection due to a relativerotational displacement between the input shaft section and the outputshaft section of the driving steering mechanism, the above apparatusforms a sensor for detecting a virtual torque by two inputs being anoutput of a steering angle (δ) sensor and a rotational angle of theservo motor, and the steering torque is determined from the virtualtorque.

Further, in Japanese Unexamined Patent Publication No. 2005-274484 A(Patent Document 2), the apparatus is equipped with the plural steeringangle sensors (three sensors) which constitute a redundant system.

THE LIST OF PRIOR ART DOCUMENTS Patent Documents

-   Patent Document 1: WO 04/022414-   Patent Document 2: Japanese Unexamined Patent Publication No.    2005-274484 A

SUMMARY OF THE INVENTION Problems to be Solved by the Invention

However, in the apparatus of Patent Document 1, although the rotorrotational information of the servo motor can back up the failure of thesteering angle sensor as the system of the backup, it is impossible todiagnose and back up the both sensors each other. Further, in theexample of the Patent Document 2, since the component around thesteering system is enlarged, the apparatus is badly affected inassembling the vehicle, and generally there is a problem of the costincreasing.

The present invention has been developed in view of the above-describedcircumstances, and an object of the present invention is to provide theelectric power steering apparatus with high quality and reasonable pricethat estimates the motor shaft angle and the steering shaft angle (thepinion side) with a high accuracy by learning the nonlinear elements ofthe mechanism system including the reduction mechanism system and thesteering system, and is possible to back up the both angle sensors byutilizing the estimating angles of the both angle sensors.

On the learning of the nonlinear elements in the mechanism system andthe steering system, factors are divided into the static characteristic,the dynamic characteristic and the delay characteristic. The presentinvention provides the electric power steering apparatus that providesthe learning styles in considering a case of “only the staticcharacteristic”, a case of “the static characteristic and the dynamiccharacteristic” and a case of “the static characteristic, the dynamiccharacteristic and the delay characteristic”, can deal with even thesteering holding of the handle, can deal with the wide operations in arange from the slow steering of the low speed steering to the high speedsteering, and further can deal with an environment variation such as atemperature and an aging variation.

Means for Solving the Problems

The present invention relates to an electric power steering apparatusthat a motor to assist-control a steering system of a vehicle isconnected to a steering shaft via a reduction mechanism, and comprises afirst angle sensor to detect a steering shaft angle of the steeringshaft and a second angle sensor to detect a motor shaft angle of themotor, the above-described object of the present invention is achievedby that comprising: a function that obtains compensation value maps byiteratively learning characteristics of nonlinear elements including thereduction mechanism based on an actual measuring angle of the firstangle sensor, an actual measuring angle of the second angle sensor, amotor torque and a motor angular speed, and estimates the steering shaftangle and the motor shaft angle by using the compensation value maps.

Further, the present invention also relates to an electric powersteering apparatus that a motor to assist-control a steering system of avehicle is connected to a steering shaft via a reduction mechanism, andcomprises a first angle sensor to detect a steering shaft angle of apinion side of the steering shaft, a second angle sensor to detect amotor shaft angle of the motor and a current detecting section to detecta motor current of the motor, the above-described object of the presentinvention is achieved by that comprising: a nonlinear logical section ofnonlinear elements to calculate compensation value maps by iterativelylearning characteristics of the nonlinear elements including thereduction mechanism, by means of a motor torque based on the motorcurrent, the steering shaft angle and a motor angular speed based on themotor shaft angle; a steering shaft angle estimating section to estimatea steering shaft estimating angle by using the compensation value mapsand the motor shaft angle; and a motor shaft angle estimating section toestimate a motor shaft estimating angle by using the compensation valuemaps and the steering shaft angle.

Effects of the Invention

In the electric power steering apparatus according to the presentinvention, in a case that one of the angle sensors is failed (includingthe abnormality), the other of the angle sensors can back up the failedangle sensor each other by obtaining the compensation value maps bymeans of learning the nonlinear elements of the mechanism systemincluding the reduction mechanism and the steering system as needed,estimating the motor shaft angle and the steering shaft angle (thepinion side) with a high accuracy based on the compensation value maps,and utilizing the estimating angles of the both angle sensors.

By performing the failure diagnosis and the function continuation bymeans of using the estimating angle of the steering shaft angle and theestimating angle of the motor shaft, it is possible to eliminate onesensor. For example, as disclosed in Patent Document 2, it is necessarythat the angle sensors are a triple system to perform the failurediagnosis and the assist-control continuation. By using the estimatingangles, the angle sensors can be a dual system in the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings:

FIG. 1 is a configuration diagram showing a general outline of anelectric power steering apparatus;

FIG. 2 is a block diagram showing a configuration example of a controlsystem of an electric power steering apparatus;

FIG. 3 is a schematic diagram showing an arrangement example of anglesensors in a steering system of the present invention;

FIG. 4 is a block diagram showing a configuration example of a controlunit (ECU) of the present invention;

FIG. 5 is a block diagram showing a configuration example (the firstembodiment) of a nonlinear learning logical section of the nonlinearelements, a steering shaft angle estimating section and a motor shaftangle estimating section according to the present invention;

FIG. 6 is a graph showing a characteristic example of a nonlinearelement static characteristic map (learning completed);

FIG. 7 is a graph showing a characteristic example of a nonlinearelement dynamic characteristic map (learning completed);

FIG. 8 is a graph showing a characteristic example of a nonlinearelement delay characteristic map (learning completed);

FIG. 9 is a graph showing a setting example of a nominal value of thenonlinear element static characteristic map;

FIG. 10 is a graph showing a setting example of a nominal value of thenonlinear element dynamic characteristic map;

FIG. 11 is a graph showing a setting example of a nominal value of thenonlinear element delay characteristic map;

FIG. 12 is a flowchart showing an example of a learning method withreference to the nonlinear elements;

FIG. 13 is a block diagram showing a configuration example of a learningsection of the static characteristic map;

FIG. 14 is a flowchart showing a learning operation example of thestatic characteristic map;

FIG. 15 is a block diagram showing a configuration example of a learningsection of the dynamic characteristic map;

FIG. 16 is a flowchart showing a learning operation example of thedynamic characteristic map;

FIG. 17 is a block diagram showing a configuration example of a learningsection of the delay characteristic map;

FIG. 18A and FIG. 18B are timing charts showing an operation example ofa multi delay section;

FIG. 19A and FIG. 19B are input-output relationship diagrams showing aprocess example of the cross correlation section;

FIG. 20 is a flowchart showing a learning operation example of the delaycharacteristic map;

FIG. 21A, FIG. 21B and FIG. 21C are characteristic diagrams explainingeffects of the first embodiment of the present invention (in cases of nocompensation, the static characteristic compensation, and the staticcharacteristic compensation, the dynamic characteristic compensation andthe delay characteristic compensation);

FIG. 22 is a block diagram showing a configuration example of the secondembodiment of the present invention;

FIG. 23 is a flowchart showing an operation example of the secondembodiment of the present invention;

FIG. 24A and FIG. 24B are characteristic diagrams explaining effects ofthe second embodiment of the present invention (in cases of nocompensation and the static characteristic compensation);

FIG. 25 is a block diagram showing a configuration example of the thirdembodiment of the present invention; and

FIG. 26 is a flowchart showing an operation example of the thirdembodiment of the present invention.

MODE FOR CARRYING OUT THE INVENTION

The present invention estimates a motor shaft angle and a steering shaftangle (a pinion side) with a high accuracy by learning nonlinearelements such as friction, backlash, an elastic coupling of a motoroutput shaft, preload to gear surfaces by means of a worm wheel and aworm, lubricating grease of a gear section and an abutting state(deflection) in a mechanism system including a reduction mechanism and asteering system as needed, and in a case that one angle sensor is failed(including an abnormality), backs up the other angle sensor andcontinues an assist-control by utilizing the estimating angles of theboth angle sensors. The backup of the failed sensor and the continuationof the assist-controlling are the features of the present invention. Abackup logic of the both angle sensors is common, and it is possible toimmediately back up the angle sensors after restarting an engine bystoring compensation value maps of nonlinear compensation identified bylearning in a nonvolatile memory such as an electrically erasableprogrammable Read-Only Memory (EEPROM).

Further, in product shipment, nominal values based on experience and thelike are stored as initial values in the nonvolatile memory such as theEEPROM. In a case that actual compensation values when an ignition keyis turning-“ON”, are out of approximate value ranges of the nominalvalues (in a case that the learning is needed), a static characteristiclearning, a dynamic characteristic learning and a delay characteristiclearning are performed. Antecedently, tuned data which are suitable forenvironments (mainly, a temperature and humidity data depending onclimate of a destination) of a destination of the vehicle (destinationcountries for export, destination regions for export and the like), areinputted as the nominal values.

Alternatively, by applying a time stamp to each of receiving detectionsignals from the respective sensors, an accurate synchronization may beperformed, an angle error may be suppressed and a steering speed whichthe steering angle detection is enabled may be higher (for example,Japanese Unexamined Patent Publication No. 2014-210471 A). In a casethat a detection period of the angle sensor disposed on the steeringshaft (for example, 500 [μs]) is different from the detection period ofa resolver, a magneto-resistive sensor (an MR sensor) or the like whichis disposed on the motor shaft (for example, 250 [μs]), the time stampis especially effective for improving the detection accuracy bysynchronizing the both angle sensors.

Embodiments according to the present invention will be described withreference to the drawings.

As shown in FIG. 3, in the present invention, a handle-side angle sensor63 to a handle side of the steering shaft 61, and a pinion-side anglesensor 64 to a pinion-side of the steering shaft 61 are disposed to atorsion bar 62 of the steering shaft 61 which is steered by the handle(a steering wheel) 60. A steering shaft angle Ap is outputted from thepinion side angle sensor 64. The steering shaft 61 is connected to themotor 66 via the reduction mechanism 65 such as the worm gears. Themotor shaft angle sensor 67 (for example, the resolver or the MR sensor)which detects a motor shaft angle Am, and a motor current detector (notshown) which detects a motor current Im are disposed on the motor 66. Anelastic coupling (not shown) is disposed at a coupling section of themotor shaft and the reduction mechanism 65.

In the present invention, the steering shaft (the pinion side) 61 isequipped with the angle sensor 64, and the motor shaft is equipped withthe angle sensor 67. In a case that one of the angle sensors is failed,it is considered that the other angle sensor backs up the one anglesensor by utilizing the detection angle of the other angle sensor, andthe assist-control is continued. The steering shaft angle Ap from thepinion-side angle sensor 64, the motor shaft angle Am from the motorshaft angle sensor 67 and the motor current Im from the motor currentdetector are inputted into a control unit (ECU) 100.

As well, although the detection angle is outputted from the handle-sideangle sensor 63, this detection angle is not directly relevant to thepresent invention and therefore the explanation is omitted.

Although the rotational shaft of the steering shaft 61 is connected tothat of the motor 66 via the reduction mechanism 65 comprising the wormand the worm gear (worm wheel), the mechanism system including thereduction mechanism 65 and the steering system include lots of nonlinearelements. That is, since the mechanism system and the steering systeminclude the nonlinear elements such as friction, backlash, an elasticcoupling of the motor output shaft, preload to the gear surfaces bymeans of the worm wheel and the worm, lubricating grease of the gearsection and the abutting state (deflection), it is impossible to back upthe failed angle sensor by simply replacing the detection value of theone of the angle sensor with that of the other of the angle sensor whenone of the angle sensors is failed. Consequently, in the presentinvention, by iteratively learning the nonlinear elements of themechanism system including the reduction mechanism 65 and the steeringsystem, the one angle sensor estimates the output angle of the otherangle sensor and vice versa. A configuration example of the control unit(ECU) 100 which performs such a function is shown in FIG. 4.

The motor current Im is inputted into a motor torque calculating section110, the calculated motor torque Tm is inputted into a nonlinearlearning logical section 130 of the nonlinear elements, the motor shaftangle Am is inputted into a motor angular speed calculating section 120and the calculated motor angular speed ωm is inputted into the nonlinearlearning logical section 130 of the nonlinear elements. An anglecompensation value MP which is calculated in the nonlinear learninglogical section 130 of the nonlinear elements is inputted into asteering shaft angle estimating section 180 and a motor shaft angleestimating section 190. A pinion-side steering shaft angle Ap isinputted into the nonlinear learning logical section 130 of thenonlinear elements and the motor shaft angle estimating section 190. Themotor shaft angle Am is inputted into the motor angular speedcalculating section 120, the nonlinear learning logical section 130 ofthe nonlinear elements and the steering shaft angle estimating section180. A steering shaft estimating angle SSe is outputted from thesteering shaft angle estimating section 180, and a motor shaftestimating angle MSe is outputted from the motor shaft angle estimatingsection 190.

Next, a relationship between a failure diagnosis of the sensors(including an abnormality diagnosis) and a backup (assist-controlcontinuation) is individually described in the following cases.

(1) A Case of Performing the Failure Diagnosis and the Backup:

In this case, it is necessary to have a dual system of the angle sensorsand the estimated estimating angle.

(1-1) A Case of the Steering Shaft Angle:

The sensor configuration is the dual system of the angle sensors of thesteering shaft (the pinion-side angle sensors 64-1 (the steering shaftangle Ap1) and 64-2 (the steering shaft angle Ap2)), and the steeringshaft estimating angle SSe is used. The failure diagnosis is performedby decision of a majority among the steering shaft angles Ap1 and Ap2,and the steering shaft estimating angle SSe. For example, in a case thatthe pinion-side angle sensor 64-1 (the steering shaft angle Ap1) isfailed, the steering shaft angle Ap2 of the pinion-side angle sensor64-2 is used for the backup (the assist control continuation).

(1-2) A Case of the Motor Shaft Angle:

The sensor configuration is the dual system of the angle sensors of themotor shaft (the motor shaft angle sensors 67-1 (the motor shaft angleAm1) and 67-2 (the motor shaft angle Am2)), and the motor shaftestimating angle MSe is used. The failure diagnosis is performed bydecision of a majority among the motor shaft angles Am1 and Am2, and themotor shaft estimating angle MSe. For example, in a case that the motorshaft angle sensor 67-1 (the motor shaft angle Am1) is failed, the motorshaft angle Am2 of the motor shaft angle sensor 67-2 is used for thebackup (the assist control continuation).

(2) A Case of Only the Failure Diagnosis:

In this case, the backup is not performed, and it is necessary to haveone angle sensor and the estimated estimating angle.

(2-1) A Case of the Steering Shaft Angle:

The sensor configuration is the steering shaft angle sensor 64 of thesteering shaft (the steering shaft angle Ap) and the steering shaftestimating angle SSe. The failure diagnosis is performed by comparingthe steering shaft angle Ap with the steering shaft estimating angleSSe. In a case that the steering shaft angle sensor 64 is failed, theassist-control is stopped.

(2-2) A Case of the Motor Shaft Angle:

The sensor configuration is the motor shaft angle sensor 67 of the motorshaft (the motor shaft angle Am) and the motor shaft estimating angleMSe. The failure diagnosis is performed by comparing the motor shaftangle Am with the motor shaft estimating angle MSe. In a case that themotor shaft angle sensor 67 is failed, the assist-control is stopped.

The angle estimating at the steering shaft angle estimating section 180and the motor shaft angle estimating section 190 is largely divided intoa static characteristic compensation and a dynamic characteristiccompensation. The static characteristic compensation is an anglecompensation of a static characteristics when the handle issteering-holding, and an angle compensation of a dynamic characteristicsin a slow steering which the handle is steered with 5 [deg/s] or lesswhen a driver drives the vehicle, stops at an intersection, and slowlyturns right or left in confirming safety. The static characteristiccompensation calculates a static characteristic compensation value CMsby a static characteristic map whose input is a motor torque Tm (or anoise-removed motor torque Tma which is passed through a low pass filter(LPF)). The dynamic characteristic compensation is an angle compensationwhen the handle is steered with some speeds (50 [deg/s] or more), in acase that the driver operates abrupt steering in suddenly appearing ahuman, and calculates an overall dynamic characteristic compensationvalue CMd which is considered a delay time depending on the motor torqueTm (the noise-removed motor torque Tma) to a dynamic characteristiccompensation value CMy by a dynamic characteristic map whose input is amotor angular speed ωm.

FIG. 5 shows a configuration example (the first embodiment) of thenonlinear learning logical section 130 of the nonlinear elements, thesteering shaft angle estimating section 180 and the motor shaft angleestimating section 190, and the nonlinear learning logical section 130of the nonlinear elements comprises a static characteristic compensatingsection 140 to calculate the compensation value CMs, a dynamiccharacteristic compensating section 150 to calculate the compensationvalue CMd and an adding section 131 to add the compensation value CMd tothe compensation value CMs and output an angle compensation value MP.The static characteristic compensating section 140 comprises a low passfilter (LPF) 141 which inputs the motor torque Tm, and a nonlinearelement static characteristic map (learning completed) 142 which inputsthe noise-removed motor torque Tma removed from high frequency noises inthe LPF 141, and outputs the compensation value CMs. The LPF 141 isrequired for preventing from an erroneous learning. The motor torque Tmis a current which is passed through the motor. Since the motor currentincludes a ripple current component, a white noise, higher harmonics andthe like, portions of large noises (peaks) are sampled (for example,every 250 [μs]), and the static characteristic map 142 can be deformedwhen the low pass filter (LPF) process is not performed. Then, the LPFwhose cutoff frequencies are in a range of 20 [Hz] to 30 [Hz] isadopted.

Furthermore, the dynamic characteristic compensating section 150comprises a nonlinear element dynamic characteristic map 151 whichinputs the motor angular speed ωm and outputs the compensation valueCMy, and a nonlinear element delay characteristic map (learningcompleted) 152 which inputs the compensation value CMy outputted fromthe nonlinear element dynamic characteristic map 151 and thenoise-removed motor torque Tma from the LPF 141, and outputs thecompensation value CMd. The compensation value CMd is added to thecompensation value CMs in the adding section 131, and the added value isoutputted as a final angle compensation value MP (the compensation valuemap in the learning).

The angle compensation value MP is subtracting-inputted into asubtracting section 181 in the steering shaft angle estimating section180, and is adding-inputted into an adding section 191 in the motorshaft angle estimating section 190. The subtracting section 181subtracts the angle compensation value MP from the motor shaft angle Am,and outputs the steering shaft estimating angle SSe. The adding section191 adds the angle compensation value MP to the steering shaft angle Ap,and outputs the motor shaft estimating angle MSe. Since the anglecompensation value MP is an angle difference between the staticcharacteristic compensating section 140 and the dynamic characteristiccompensating section 150, and the motor torque Tm and the motor angularspeed ωm, which are a motor reference, are inputted into the staticcharacteristic compensating section 140 and the dynamic characteristiccompensating section 150, respectively, the angle compensation value MPis subtracting-inputted into the steering shaft angle estimating section180 and is adding-inputted into the motor shaft angle estimating section190.

Thereafter, it is performed the diagnoses whether errors (absolutevalues) between the steering shaft estimating angle SSe and the motorshaft estimating angle MSe and the respective actual measuring valuesare within a tolerance range ε or not, and the learning is repeateduntil the errors become within the tolerance range ε. The learning iscompleted at the time when the errors are within the tolerance range ε.That is, the diagnoses are performed in accordance with a followingEquation 1. When the Equation 1 is satisfied, the learning is completed,and when the Equation 1 is not satisfied, the learning is repeated inthe predetermined number (for example, twice). In the Equation 1, it isjudged whether the absolute value of the difference between the steeringshaft estimating angle SSe and the steering shaft angle Ap is within atolerance range ε1 or not, and whether the absolute value of thedifference between the motor shaft estimating angle MSe and the motorshaft angle Am is within a tolerance range ε2 or not. By repeating thelearning, the accuracy of the estimating angle can be higher, and it ispossible to handle with surrounding environmental variations (thetemperature and the humidity), the aging variations of the mechanismcomponents and the like. The tolerance range ε1 may be equal to thetolerance range ε2 (ε1=ε2).|SSe−AP|≤ε1|MSe−Am|≤ε2  [Equation 1]

As well, in a case that both or one of inequalities in the Equation 1 isnot satisfied even when repeating the learning, it is judged that one ofthe steering system and the sensor system is failed or is abnormal.

As shown in FIG. 6, when the motor torque Tm is larger from Tm1 (=0) ina positive or negative direction, the nonlinear element staticcharacteristic map 142 has a characteristic, which the compensationvalue (CMs) that is the angle error gradually and nonlinearly becomeslarger. The compensation value (CMs) steeply increases or decreases nearthe motor torque Tm1 (=0). As shown in FIG. 7, when the motor angularspeed ωm is higher from zero in the positive or negative direction, thenonlinear element dynamic static characteristic map 151 has acharacteristic, which the compensation value (CMy) that is the angleerror gradually and nonlinearly becomes larger. The compensation value(CMy) has a substantially flat characteristic when the motor angularspeed ωm is in a range of “−ωm1” to “+ωm2” (for example, 50 [deg/s]),which is near zero. The static characteristic can be covered at the flatportion. Since viscosity decreases when the temperature is high, thecompensation value also decreases as shown in a dashed line.

Further, as shown in FIG. 8, when the motor torque Tma from the LPF 141is larger, the nonlinear element delay characteristic map 152 has acharacteristic, which the compensation value (CMd) that is a phase errorgradually and nonlinearly becomes smaller. The compensation value (CMd)steeply decreases near the motor torque Tma1(0), and finally convergesalmost zero when the motor torque Tma is larger. In the temperaturecharacteristic of the delay characteristic map 152, since the viscositydecreases when the temperature is high, the compensation value alsodecreases as shown in the dashed line.

The learning of respective characteristic maps (142, 151 and 152) iscorresponding to creating the maps. As the maps are learned in the widerange (for example, from one (a positive side) of the rack endneighborhood to the other (a negative side) of the rack endneighborhood) against horizontal axes (the motor torque Tm, the motorangular speed ωm and the motor torque Tma), the error becomes small.That is, it is meaningless that the learning is only a particular point(for example, in FIG. 6, near a point s₅ (Tm1=0, CMs=0)). Since thepoint numbers of the maps are depending on capacities of a Random AccessMemory (RAM) and a Read Only Memory (ROM) of a microcomputer, and anarithmetic speed of a CPU, it cannot be concluded against the pointnumbers of the maps. When the point numbers which are some extent rangeare covered against the horizontal axes, it is judged that the learningis completed. In the examples of FIG. 6 to FIG. 8, each of thehorizontal axes is divided into ten portions, and it is judged that thelearning is completed when the learning is performed at eleven points.

As well, in a region which the characteristic variation is large, thelearning is performed with an interval as narrowly as possible, and in aregion which the characteristic variation is small, the learning isperformed with a wide interval.

In angle estimating of respective components in the electric powersteering apparatus, it is necessary to compensate the above allnonlinear elements such as the friction and the backlash in themechanism system including the reduction mechanism 65 and the steeringsystem. For performing the compensation, at least the staticcharacteristic learning is requested, and the dynamic characteristiclearning is preferably performed after the static characteristiclearning. Further, the delay learning can be performed.

In the product shipment, since the learning data cannot be acquired, asshown in FIG. 9 to FIG. 11, nominal values based on the experience andthe like are antecedently stored in the nonvolatile memory such as theEEPROM. When the actual data are out of the approximate value ranges ofthe nominal values as shown in the dashed lines, the learning isperformed. Antecedently, the tuned data which are suitable forenvironments (mainly, the temperature and the humidity data depending onthe climate of a destination) of a destination of the vehicle (thedestination country for export, the destination region for export andthe like), are adopted as the nominal data. FIG. 9 is a graph whichshows a setting example of the nominal values (ns₀ to ns₁₀) of thenonlinear element static characteristic map, and the dashed linesrepresent the approximate range which judges whether the learning isneeded or not. FIG. 10 is a graph which shows the setting example of thenominal values (ny₀ to ny₁₀) of the nonlinear element dynamiccharacteristic map, and the dashed lines represent the approximate rangewhich judges whether the learning is needed or not. FIG. 11 is a graphwhich shows the setting example of the nominal values (nd₀ to nd₁₀) ofthe nonlinear element delay characteristic map, and the dashed linesrepresent the approximate range which judges whether the learning isneeded or not.

Here, in the first embodiment, the dynamic characteristic learning isperformed after the static characteristic learning, and further thedelay learning is performed. An overall operation example (the firstembodiment) which performs the angle estimating based on these learningresults will be described with reference to a flowchart of FIG. 12.

At first, when the ignition key is turned “ON”, the angle detection isperformed (Step S1), and it is judged whether the calculatedcompensation value is within the approximate value range of the nominalvalue as shown in FIG. 9, or not (Step S2). In the example of FIG. 9,the compensation values A1 and A2 are out of the range, and thecompensation values B1 and B2 are within the range. In a case that it isjudged that the compensation value is out of the approximate valuerange, the learning of the nonlinear element static characteristic map142 is performed (Step S10), and the above learning is continued untilthe learning is completed (Step S20). When the static characteristic mapcan sufficiently be learned (for example, FIG. 6) to the motor torqueregion of the electric power steering apparatus, the learning iscompleted. After completing the learning of the nonlinear element staticcharacteristic map 142, the learning of the nonlinear element dynamiccharacteristic map 151 (Step S30) and the learning of the nonlinearelement delay characteristic map 152 (Step S50) are performed inparallel.

In the learning of the nonlinear element dynamic characteristic map 151(Step S30) and the learning of the nonlinear element delaycharacteristic map 152 (Step S50), it is judged whether the compensationvalues are within the respective approximate value ranges of the nominalvalues as shown in FIG. 10 and FIG. 11, or not. When the compensationvalues are out of the approximate value ranges, the learning isperformed. However, these judgements are omitted, and the learning maybe performed. In FIG. 12, the judgement operation is omitted.

Normally, after learning the nonlinear element dynamic characteristicmap 151, the learning of the nonlinear element delay characteristic map152 is performed. The learning of the nonlinear element dynamiccharacteristic map 151 (Step S30) is continued until the learning iscompleted (for example, FIG. 7) (Step S40). The learning of thenonlinear element delay characteristic map 152 (Step S50) is continueduntil the learning is completed (for example, FIG. 8) (Step S60). Thelearning is completed when the dynamic characteristic map 151 cansufficiently learned for the region of the motor angular speed ω of theelectric power steering apparatus. The learning is also completed whenthe delay characteristic map 152 can sufficiently learned for the regionof the motor torque Tma.

When all of the map learning, which are the learning of the nonlinearelement dynamic characteristic map 151 and the nonlinear element delaycharacteristic map 152, are completed (Step S70), the compensation valuemap is created, the angle compensation value MP is calculated by addingthe compensation value CMd from the dynamic characteristic compensatingsection 150 to the compensation value CMs from the static characteristiccompensating section 140 in the adding section 131, and the estimatingangle is estimated based on the angle compensation value MP (Step S71).The steering shaft estimating angle SSe is calculated by subtracting theangle compensation value MP from the motor shaft angle Am, and the motorshaft estimating angle MSe is calculated by adding the anglecompensation value MP to the steering shaft angle Ap. Then, it isdiagnosed whether the errors (absolute values) between the estimatingangles and the actual measuring values are within the tolerance range εor not in accordance with the above Equation 1 or not (Step S72), andthe learning is completed when the errors are within the tolerance rangeε. In a case that the errors are larger than the tolerance range ε, itis judged whether the iteration number is “N” times (for example, threetimes) or not (Step S80), and in a case that the iteration number isless than “N” times, the process is returned to the above step S10 andthe above process is repeated.

At the above Step S80, in a case that the iteration number is “N” times,it is judged that the steering system or the sensor system is failed(Step S81). A setting of the iteration number “N” of the above Step S80can appropriately be changeable.

By learning iteratively, the accuracy of the steering shaft estimatingangle SSe and the motor shaft estimating angle MSe can be higher, and itis possible to deal with the environment variation such as thetemperature and the aging deterioration of the mechanism components.Although the present embodiment deals with the environment variationsuch as the temperature by learning iteratively, a temperature sensor isprovided additionally, and the values of respective maps may becorrected depending on the detected temperature.

Required input signals in the above learning are the motor torque Tm,the motor angular acceleration αm, the motor angular speed ωm, the motorshaft angle Am and the steering shaft angle Ap.

Next, the learning of the nonlinear element static characteristic map142 at the above Step S10 will be described.

As shown in FIG. 6, the horizontal axis is the motor torque Tm, and thevertical axis is the compensation value CMs being an angle deviationbetween the motor shaft angle Am and the steering shaft angle Ap in thenonlinear element static characteristic map 142. The configuration ofthe nonlinear element static characteristic map 142 comprises a staticcharacteristic learning judging section 143 and a static characteristiclearning logical section 144, for example as shown in FIG. 13.

The motor angular speed ωm is inputted into the static characteristiclearning judging section 143, and the static characteristic learningjudging section 143 outputs a learning judging signal LD1 (“ON” or“OFF”) in accordance with the judging described below. The staticcharacteristic learning logical section 144 comprises a subtractingsection 144-1, addition averaging sections 144-2 and 144-3, and anonlinear element static characteristic map creating section 144-4. Thenoise-removed motor torque Tma which is removed from the noise in theLPF 141 is inputted into the addition averaging section 144-2. Thesteering shaft angle Ap and the motor shaft angle Am are inputted intothe subtracting section 144-1, and the angle error is inputted into theaddition averaging section 144-3. The learning judging signal LD1 isinputted into the static characteristic learning logical section 144,and addition averaging values MN1 and MN2, which are calculated in theaddition averaging sections 144-1 and 144-2, respectively, are inputtedinto the nonlinear element static characteristic map creating section144-4.

In such a configuration, the operation example (the staticcharacteristic map learning) will be described with reference to theflowchart of FIG. 14.

When the handle is in the steering holding state or is the slow steeringwhich is equal to or less than 5 [deg/s] (the motor angular speed ωm isan almost zero state), that is, when the static characteristic learningjudging section 143 judges that the motor angular speed ωm is an almostzero state and turns-“ON” the learning judging signal LD1, and thelearning judging signal LD1 which indicates “ON” is inputted into thestatic characteristic learning logical section 144, the staticcharacteristic learning of the static characteristic learning logicalsection 144 starts (Step S11). When the learning is started, thedeviation Dp between the steering shaft angle Ap and the motor shaftangle Am is calculated in the subtracting section 144-1 (Step S12). Thedeviation Dp is inputted into the addition averaging section 144-3, andthe addition averaging value MN2 is calculated in the addition averagingsection 144-3 (Step S13). The noise-removed motor torque Tma from theLPF 141 is also inputted into the addition averaging section 144-2, andthe addition averaging value MN1 is calculated in the addition averagingsection 144-2 (Step S14). The calculating order of the additionaveraging values MN1 and MN2 may be changeable. The addition averagingvalues MN1 and MN2 are inputted into the nonlinear element staticcharacteristic map creating section 144-4 (corresponding to thenonlinear element static characteristic map 142 of FIG. 5), and thenonlinear element static characteristic map 142 is updated by using acalculating method such as an iterative least squares method or the like(Step S15).

Thus, when the steering holding state or the slow steering is continuedfor a constant time, the nonlinear element static characteristic map 142is updated by using the calculating method such as the iterative leastsquares method. When the static characteristic map can sufficiently belearned to the motor torque region of the electric power steeringapparatus, the learning is completed.

Next, the learning of the dynamic characteristic map at the above StepS30 will be described.

As shown in FIG. 7, the horizontal axis is the motor angular speed ωm,and the vertical axis is the compensation value CMy being an angledeviation between the motor shaft angle Ams (after the staticcharacteristic compensation) and the steering shaft angle Ap in thedynamic characteristic map. The configuration of the nonlinear elementdynamic characteristic map 151 is shown, for example, in FIG. 15.

The motor angular acceleration am is inputted into the dynamiccharacteristic learning judging section 145, the motor torque Tm isinputted into the dynamic characteristic learning judging section 145and the nonlinear element static characteristic map 146-1 via the LPF141. The dynamic characteristic learning judging section 145 outputs alearning judging signal LD2 (“ON” or “OFF”) when a predeterminedcondition (the motor angular acceleration am is almost zero and themotor torque Tm (Tma) is large to some degree) is satisfied. The dynamiccharacteristic learning logical section 146 comprises the nonlinearelement static characteristic map 146-1, an adding section 146-2, asubtracting section 146-3, addition averaging sections 146-4 and 146-5,and a nonlinear element dynamic characteristic map creating section146-6 (corresponding to the nonlinear element dynamic characteristic map151 in FIG. 5). The motor angular speed ωm is inputted into the additionaveraging section 146-4, the steering shaft angle Ap is adding-inputtedinto the subtracting section 146-3, and the motor shaft angle Am isinputted into the adding section 146-2. The compensation value CMs fromthe nonlinear element static characteristic map 146-1 is inputted intothe adding section 146-2, and the added value (the motor shaft angleafter the static characteristic compensation) Ams issubtracting-inputted into the subtracting section 146-3. A deviation Dm(=Ap−Ams), which is calculated in the subtracting section 146-3, isinputted into the addition averaging section 146-5. The learning judgingsignal LD2 is inputted into the dynamic characteristic learning logicalsection 146, and the addition averaging values MN3 and MN4, which arecalculated in the addition averaging sections 146-4 and 146-5,respectively, are inputted into the dynamic characteristic map creatingsection 146-6.

In such a configuration, the operation example (the dynamiccharacteristic map learning) will be described with reference to theflowchart of FIG. 16.

When the motor angular acceleration am is almost zero, the worm gear istightly engaged with the motor gear (the noise-removed motor torque Tmafrom the LPF 141 is large to some degree), the learning judging signalLD2 is turned “ON” and is inputted into the dynamic characteristiclearning logical section 146, and the dynamic characteristic learning ofthe dynamic characteristic learning logical section 146 is started (StepS31). When the learning is started, the noise-removed motor torque Tmafrom the LPF 141 is inputted into the nonlinear element staticcharacteristic map 146-1, and the static characteristic compensation isperformed (Step S32). The compensation value CMs of the staticcharacteristic compensation is inputted into the adding section 146-2,the added value Ams, which is added the motor shaft angle Am after thestatic characteristic compensation to the compensation value CMs, iscalculated and is subtracting-inputted into the subtracting section146-3. The deviation Dm (=Ap−Ams) between the steering shaft angle Apand the added value Ams is calculated in the subtracting section 146-3(Step S33), and is inputted into the addition averaging section 146-5.The addition averaging value MN4 is calculated in the addition averagingsection 146-5 (Step S34).

The motor angular speed ωm is also inputted into the addition averagingsection 146-4, and the addition averaging value MN3 is calculated in theaddition averaging section 146-4 (Step S35). The calculation order ofthe addition averaging values MN3 and MN4 may be changeable. Theaddition averaging values MN3 and MN4 are inputted into the nonlinearelement dynamic characteristic map creating section 146-6. When thelearning condition is continued for a constant time, the nonlinearelement dynamic characteristic map 151 is updated by using thecalculation method such as the iterative least squares method (StepS36). Thus, when the dynamic characteristic map can sufficiently belearned to the motor angular speed region of the electric power steeringapparatus, the learning is completed.

Next, the learning of the delay characteristic map at the above Step S50will be described.

As shown in FIG. 8, the horizontal axis is the motor torque Tma, and thevertical axis is the compensation value CMd being a phase deviationbetween the motor shaft angle Ams (after the compensation of the staticcharacteristic compensation) and the steering shaft angle Ap. Theconfiguration of the delay characteristic map is shown, for example, inFIG. 17.

The noise-removed motor torque Tma via the LPF 141 is inputted into adelay characteristic learning judging section 147 and the nonlinearelement static characteristic map 148-1. The learning judging signal LD3(“ON” or “OFF”) is outputted from the delay characteristic learningjudging section 147 when a predetermined condition (when the motortorque Tm (Tma) is equal to or less than a predetermined value) issatisfied. The learning judging signal LD3 is inputted into a delaycharacteristic learning logical section 148. The delay characteristiclearning logical section 148 comprises the nonlinear element staticcharacteristic map 148-1 (the map 146-1 in FIG. 15), an adding section148-2, a subtracting section 148-3, an addition averaging section 148-5,a multi delay section 148-4, a cross correlation section 148-6 and anonlinear element delay characteristic map creating section 148-7. Themotor torque Tm is inputted into the addition averaging section 148-5,the motor angular speed ωm is inputted into the multi delay section148-4, and the multi delay output MD is inputted into the crosscorrelation section 148-6. The steering shaft angle Ap isadding-inputted into the subtracting section 148-3, and the motor shaftangle Am is inputted into the adding section 148-2. The compensationvalue CMs from the nonlinear element static characteristic map 148-1 isinputted into the adding section 148-2, and the added value (the motorshaft angle after the static characteristic compensation) Ams issubtracting-inputted into the subtracting section 148-3. A deviation Dd,which is calculated in the subtracting section 148-3, is inputted intothe cross correlation section 148-6. The cross correlation section 148-6performs a cross correlation process based on the multi delay output MDfrom the multi delay section 148-4 and the deviation Dd, and searches adelay time which the correlation is the highest. Since the crosscorrelation analyzes similarity of two input signals, a certain amountof analysis time is required.

FIG. 18A and FIG. 18B show an operation example of the multi delaysection 148-4, and the multi delay section 148-4 outputs the multi delaymotor angular speeds ωd0, ωd1, . . . , ωd10 which have respectivepredetermined delay times by each of delay devices (Z⁻¹) to the input ofthe motor angular speed ωm. The multi delay motor angular speeds MD (ωd0to ωd10), which are outputted from the multi delay section 148-4, andthe steering shaft angle (after the static characteristic compensation)Dd are inputted into the cross correlation section 148-6. As shown inFIG. 19A and FIG. 19B, the cross correlation section 148-6 calculatesthe correlation functions by using the steering shaft angle Dd as areference signal and the multi delay motor angular speeds MD (ωd0 toωd10) from the multi delay section 148-4, and the delay time of themulti delay device having the largest correlation is reflected to themap.

A learning judging signal LD3 is inputted into the delay characteristiclearning logical section 148, an addition averaging value MN5 which iscalculated in the addition averaging section 148-5, and the crosscorrelation value ML which is the output of the cross correlationsection 148-6 are also inputted into the nonlinear element delaycharacteristic map creating section 148-7.

In such a configuration, the operation example (the delay characteristicmap learning) will be described with reference to a flowchart of FIG.20.

In a region which the motor torque Tma (or Tm) is small, since aninfluence of the backlash is large, the delay time is long. On the otherhand, in a region which the motor torque Tma (or Tm) is large, since theworm gear is tightly engaged with the motor gear, the delay time isshort.

When the learning is started, the noise-removed motor torque Tma fromthe LPF 141 is inputted into the nonlinear element static characteristicmap 148-1, and the static characteristic compensation by means of thenonlinear element static characteristic map 148-1 is performed (StepS52). The compensation value CMs of the static characteristiccompensation is inputted into the adding section 148-2. An added value(the motor shaft angle after the static characteristic compensation)Ams, which is added the motor shaft angle Am to the compensation valueCMs, is subtracting-inputted into the subtracting section 148-3, and thedeviation Dd, which is subtracted the added value Ams from the steeringshaft Ap, is calculated in the subtracting section 148-3 (Step S53), andis inputted into the cross correlation section 148-6. The motor angularspeed ωm is inputted into the multi delay section 148-4, and the multidelay section 148-4 calculates the plural multi delay motor angularspeeds MD (ωd0 to ωd10) which have a different delay time (Step S54).The multi delay motor angular speeds MD are inputted into the crosscorrelation section 148-6, and the cross correlation process isperformed (Step S55). The cross correlation section 148-6 searches thedelay time which the correlation is the largest in the plural multidelay motor angular speeds which the delay amounts are different, andoutputs the correlation coefficients ML.

Further, the motor torque Tma is inputted into the addition averagingsection 148-5, and the addition averaging value MN5 is calculated (StepS56). The calculation order of the addition averaging value MN5 and thecorrelation coefficients ML may be changeable. The addition averagingvalue MN5 and the correlation coefficients ML are inputted into thenonlinear element delay characteristic map creating section 148-7. Whenthe learning condition is continued for a constant time, the delaycharacteristic map 152 is updated by using the calculating method suchas the iterative least squares method (Step S57). Then, when the delaycharacteristic map can sufficiently be learned to the motor torqueregion of the electric power steering apparatus, the learning iscompleted.

The static characteristic map 142 in FIG. 5 is corresponding to thestatic characteristic map creating section 144-4 of FIG. 13, the dynamiccharacteristic map 151 in FIG. 5 is corresponding to the dynamiccharacteristic map creating section 146-6 in FIG. 15, and the delaycharacteristic map 152 in FIG. 5 is corresponding to the delaycharacteristic map creating section 148-7 in FIG. 17. FIG. 5 shows themaps which the learning is completed, and FIG. 13, FIG. 15 and FIG. 17show the maps in learning. In this connection, the maps are designatedwith different reference numerals.

Next, the effect of the present invention (the first embodiment) will bedescribed with reference to FIG. 21A, FIG. 21B and FIG. 21C.

The horizontal axis is the time and the vertical axis is the angle error(the difference between the motor shaft angle and the steering shaftangle). The state that the handle is steered to left or right around thehandle center is shown at an interval from a time point t0 to a timepoint t1. The state that the handle is steered near the left-side endand then is steered to left or right is shown at the interval from thetime point t1 to a time point t2. The state that the handle is steerednear the right-side end and then is steered to left or right is shown atthe interval from the time point t2 to a time pint t3. The state thatthe handle is returned to the center is shown after the time point t3.As shown in FIG. 21A, in a case that the compensation is not performed,the angle difference is large (2.5°). As shown in FIG. 21B, byperforming the static characteristic compensation, the angle differenceis reduced (0.75°) to the motion that the handle is steered in the lowspeed. Further, as shown in FIG. 21C, by appending the dynamiccharacteristic compensation, the angle difference can be reduced (0.25°)to even the motion that the handle is steered in the high speed.

In the above-described first embodiment, as shown in FIG. 5, thecompensations of the static characteristic, the dynamic characteristicand the delay characteristic are performed. However, the compensation ofthe static characteristic may only be performed in a configuration shownin FIG. 22 (the second embodiment).

The static characteristic compensating section 140 that calculates thecompensation value CMs comprises the low pass filter (LPF) 141 to inputthe motor torque Tm, and the nonlinear element static characteristic map(learning completed) 142 to input the noise-removed motor torque Tmawhich is removed from the high frequency noise in the LPF 141, andoutputs the compensation value CMs (or the compensation map inlearning).

As well as the first embodiment, the compensation value CMs issubtracting-inputted into a subtracting section 181 and isadding-inputted into an adding section 191. The subtracting section 181outputs the steering shaft estimating angle SSe, and the adding section191 outputs the motor shaft estimating angle MSe. Then, it is diagnosedwhether the errors (absolute values) between the steering shaftestimating angle SSe and the motor shaft estimating angle MSe and therespective actual measuring values are within the tolerance range ε ornot, and the learning is repeated until the errors are within thetolerance range ε. The learning is completed at the time when the errorsare within the tolerance range ε.

As shown in FIG. 6, the nonlinear element static characteristic map 142has the characteristic, which the compensation value (CMs) that is theangle error gradually and nonlinearly becomes larger, when the motortorque Tm is larger from Tm1 (=0) in a positive or negative direction.The compensation value (CMs) steeply increases or decreases near themotor torque Tm1 (=0).

The learning of the nonlinear element static characteristic map 142 iscorresponding to creating the map. As the map is learned in the widerange (for example, from one (the positive side) of the rack endneighborhood to the other (the negative side) of the rack endneighborhood) against the horizontal axis (the motor torque Tm), theerror becomes small. That is, it is meaningless that the learning isonly a particular point (for example, in FIG. 6, near a point s₅ (Tm1=0,CMs=0)). Since the point numbers of the map are depending on capacitiesof the RAM and the ROM of the microcomputer, and the arithmetic speed ofthe CPU, it cannot be concluded against the point numbers of the map.When the point numbers which are some extent range are covered againstthe horizontal axis, it is judged that the learning is completed.

An overall operation example which performs the angle estimating basedon the learning and the learning result of the static characteristicwill be described with reference to a flowchart of FIG. 23. Even in thiscase, the learning may be started by judging that the compensation valueis out of the approximate value range of the nominal value, as shown inFIG. 9.

At first, the learning of the nonlinear element static characteristicmap 142 is performed (Step S10), and the learning is continued until thelearning is completed (Step S101). When the static characteristic mapcan sufficiently be learned (for example, FIG. 6) to the motor torqueregion of the electric power steering apparatus, the learning iscompleted. Since the compensation value map is created by completing thelearning of the nonlinear element static characteristic map 142, theestimating angle is estimated based on the compensation value CMs fromthe static characteristic compensating section 140 (Step S102). Thesteering shaft estimating angle SSe is calculated by subtracting thecompensation value CMs from the motor shaft angle Am. The motor shaftestimating angle MSe is calculated by adding the compensation value CMsto the steering shaft angle Ap. Then, it is diagnosed whether the errors(absolute values) between the estimating angles and the actual measuringvalues are within the tolerance range ε or not in accordance with theabove Equation 1 or not (Step S103), and the learning is completed whenthe errors are within the tolerance range ε. In a case that the errorsare larger than the tolerance range ε, it is judged whether theiteration number is for example, three times or not (Step S104), and ina case that the iteration number is equal to or less than twice, theprocess is returned to the above Step S10 and the above process isiterated.

At the above Step S104, in a case that the iteration number is threetimes, it is judged that the steering system or the sensor system isfailed (Step S105). A setting of the iteration number of the above StepS104 can appropriately be changeable.

By learning iteratively, the accuracy of the steering shaft estimatingangle SSe and the motor shaft estimating angle MSe can be higher, and itis possible to deal with an environment variation such as a temperatureand aging deterioration of the mechanism components. Although theembodiment deals with the environment variation such as the temperatureby learning iteratively, a temperature sensor is provided additionally,and the values of respective maps may be corrected depending on thedetected temperature. The learning operation of the nonlinear elementstatic characteristic map 142 at the above Step S10 is similar to thatof FIG. 14.

Next, the effect of the second embodiment will be described withreference to FIG. 24A and FIG. 24B.

The state that the handle is steered to left or right around the handlecenter is shown at an interval from a time point t0 to a time point t1.The state that the handle is steered near the left-side end and then issteered to left or right is shown at the interval from the time point t1to a time point t2. The state that the handle is steered near theright-side end and then is steered to left or right is shown at theinterval from the time point t2 to a time point t3. The state that thehandle is returned to the center is shown after the time point t3. Asshown in FIG. 24A, in a case that the compensation is not performed, theangle difference is large (2.5°). As shown in FIG. 24B, by performingthe static characteristic compensation, the angle difference is reduced(0.75°) to the motion that the handle is steered in the low speed.

FIG. 25 shows a configuration example of a third embodiment whichperforms the static characteristic compensation and the dynamiccharacteristic compensation (no delay characteristic compensation). Thenonlinear learning logic section 130 of the nonlinear elements comprisesthe static characteristic compensating section 140 to calculate thecompensation value CMs, the dynamic characteristic compensating section150 to calculate the compensation value CMy, and the adding section 131to add the compensation value CMy to the compensation value CMs andoutput the angle compensation value MP. The static characteristiccompensating section 140 comprises the low pass filter (LPF) 141 toinput the motor torque Tm, and the nonlinear element staticcharacteristic map (learning completed) 142 to input the noise-removedmotor torque Tma which is removed from the high frequency noise in theLPF 141, and output the compensation value CMs. The dynamiccharacteristic compensating section 150 comprises the nonlinear elementdynamic characteristic map 151 that inputs the motor angular speed ωmand outputs the compensation value CMy. The compensation value CMy isadded to the compensation value CMs in the adding section 131, and theadded value is outputted as the final angle compensation value MP (thecompensation value map in learning).

The angle compensation value MP is subtracting-inputted into thesubtracting section 181, and is adding-inputted into the adding section191 of the motor shaft angle estimating section 190. The subtractingsection 181 outputs the steering shaft angle SSe, and the adding section191 outputs the motor shaft angle MSe. Then, it is diagnosed whether theerrors (absolute values) between the steering shaft estimating angle SSeand the motor shaft estimating angle MSe and the respective actualmeasuring values are within the tolerance range ε or not, in accordancewith the above Equation 1, and the learning is repeated until the errorsare within the tolerance range ε. The learning is completed at the timewhen the errors are within the tolerance range ε.

As well, in a case that both or one of inequalities in the Equation 1 isnot satisfied even when iterating the learning, it is judged that one ofthe steering system and the sensor system is failed or is abnormality.The nonlinear element static characteristic map 142 has thecharacteristic shown in FIG. 6, and the nonlinear element dynamiccharacteristic map 151 has the characteristic shown in FIG. 7.

The learning of respective characteristic maps (142 and 151) iscorresponding to creating the maps. In angle estimating of therespective components in the electric power steering apparatus, it isnecessary to compensate the nonlinear elements such as the friction andthe backlash of the mechanism system including the reduction mechanism65 and the steering system. For performing the compensation, at leastthe static characteristic learning is requested, and the dynamiccharacteristic learning is preferably performed after the staticcharacteristic learning.

Here, the dynamic characteristic learning is performed after the staticcharacteristic learning, and an overall operation example (the thirdembodiment) which performs the angle estimating based on these learningresults will be described with reference to a flowchart of FIG. 26. Evenin this case, the learning may be started by judging that thecompensation values are out of the approximate value ranges of therespective nominal values, as shown in FIG. 9 and FIG. 10.

At first, the learning of the nonlinear element static characteristicmap 142 is performed (Step S10), and the learning is continued until thelearning is completed (Step S20). When the static characteristic map cansufficiently be learned (for example, FIG. 6) to the motor torque regionof the electric power steering apparatus, the leaning is completed. Thelearning of the nonlinear element dynamic characteristic map 151 (StepS30) is performed after completing the learning of the nonlinear elementstatic characteristic map 142. The learning of the nonlinear elementdynamic characteristic map 151 (Step S30) is continued until thelearning is completed (for example, FIG. 7) (Step S40). When the dynamiccharacteristic map can sufficiently be learned to the motor angularspeed region of the electric power steering apparatus, the leaning iscompleted.

When the learning of the nonlinear element dynamic characteristic map151 is completed, the compensation value maps are created, the anglecompensation value MP is calculated by adding the compensation value CMyfrom the dynamic characteristic compensating section 150 to thecompensation value CMs from the static characteristic compensatingsection 140 in the adding section 131, and the estimating angle isestimated based on the angle compensation value MP (Step S110). Thesteering shaft estimating angle SSe is calculated by subtracting theangle compensation value MP from the motor shaft angle Am, and the motorshaft estimating angle MSe is calculated by adding the anglecompensation value MP to the steering shaft angle Ap. Then, it isdiagnosed whether the errors (absolute values) between the estimatingangles and the actual measuring values are within the tolerance range εor not in accordance with the above Equation 1 or not (Step S111), andthe learning is completed when the errors are within the tolerance rangeε. In a case that the errors are larger than the tolerance range ε, itis judged whether the iteration number is, for example, three times ornot (Step S112), and in a case that the iteration number is equal to orless than twice, the process is returned to the above Step S10 and theabove process is repeated.

At the above Step S112, in a case that the iteration number is threetimes, it is judged that the steering system or the sensor system isfailed (Step S113). A setting of the iteration number of the above StepS112 can appropriately be changeable.

By learning iteratively, the accuracy of the steering shaft estimatingangle SSe and the motor shaft estimating angle MSe can be higher, and itis possible to deal with the environment variation such as a temperatureand aging deterioration of the mechanism components. Although thepresent embodiment deals with the environment variation such as thetemperature by learning iteratively, a temperature sensor is providedadditionally, and the values of respective maps may be correcteddepending on the detected temperature.

The learning operation of the nonlinear element static characteristicmap 142 at the above Step S10 is similar to that of FIG. 14, and thelearning operation of the nonlinear element dynamic characteristic map151 at the above Step S30 is similar to that of FIG. 16.

In the above embodiments, the column-type electric power steeringapparatus is described, and the present invention can be applied to adownstream-type electric power steering apparatus.

EXPLANATION OF REFERENCE NUMERALS

-   1, 60 handle (steering wheel)-   2, 61 steering shaft (column shaft, handle shaft)-   10 torque sensor-   12 vehicle speed sensor-   13 battery-   20, 66 motor-   21 rotational sensor-   30, 100 control unit (ECU)-   62 torsion bar-   63 handle-side angle sensor-   64 pinion-side angle sensor-   64 reduction mechanism-   67 motor shaft angle sensor-   110 motor torque calculating section-   120 motor angular speed calculating section-   130 nonlinear learning logical section of nonlinear elements-   140 static characteristic compensating section-   141 low pass filter (LPF)-   142 nonlinear element static characteristic map-   150 dynamic characteristic compensating section-   151 nonlinear element dynamic characteristic map-   152 nonlinear element delay characteristic map-   180 steering shaft angle estimating section-   190 motor shaft angle estimating section

The invention claimed is:
 1. An electric power steering apparatus that amotor to assist-control a steering system of a vehicle is connected to asteering shaft via a reduction mechanism, and comprises a first anglesensor to detect a steering shaft angle of said steering shaft and asecond angle sensor to detect a motor shaft angle of said motor,comprising: a function that obtains compensation value maps byiteratively learning characteristics of nonlinear elements includingsaid reduction mechanism based on an actual measuring angle of saidfirst angle sensor, an actual measuring angle of said second anglesensor, a motor torque and a motor angular speed, and estimates saidsteering shaft angle and said motor shaft angle by using saidcompensation value maps.
 2. The electric power steering apparatusaccording to claim 1, further comprising: a diagnosis function tomutually diagnose failures of said first angle sensor and said secondangle sensor by comparing an estimating angle of said steering shaftangle and an estimating angle of said motor shaft with an actualmeasuring angle of said first angle sensor and an actual measuring angleof said second angle sensor, respectively.
 3. The electric powersteering apparatus according to claim 2, wherein, when it is judged thatsaid first angle sensor or said second angle sensor is failed by meansof said diagnosis function, said assist-control is continued based on anestimating angle of said motor shaft or an estimating angle of saidsteering shaft angle.
 4. The electric power steering apparatus accordingto claim 1, wherein said learning is performed by a staticcharacteristic learning when a handle is a steering holding or a lowspeed steering with a predetermined speed or less, and by a dynamiccharacteristic learning when said handle is steered in a high speed witha predetermined speed or more.
 5. The electric power steering apparatusaccording to claim 4, wherein, when initial values based on an actualmeasuring angle of said first angle sensor, an actual measuring angle ofsaid second angle sensor, said motor torque and said motor angular speedare out of approximate value ranges of respective characteristic nominalvalues, said static characteristic learning and said dynamiccharacteristic learning are performed.
 6. The electric power steeringapparatus according to claim 1, wherein said learning is performed bymeans of a static characteristic learning when a handle is a steeringholding or a slow steering with a predetermined speed or less.
 7. Theelectric power steering apparatus according to claim 2, wherein saidlearning is performed by means of a static characteristic learning whena handle is a steering holding or a slow steering with a predeterminedspeed or less.
 8. The electric power steering apparatus according toclaim 3, wherein said learning is performed by means of a staticcharacteristic learning when a handle is a steering holding or a slowsteering with a predetermined speed or less.
 9. The electric powersteering apparatus according to claim 8, wherein, when initial valuesbased on an actual measuring angle of said first angle sensor, an actualmeasuring angle of said second angle sensor, said motor torque and saidmotor angular speed are out of approximate value ranges of respectivecharacteristic nominal values, said static characteristic learning isperformed.
 10. The electric power steering apparatus according to claim7, wherein, when initial values based on an actual measuring angle ofsaid first angle sensor, an actual measuring angle of said second anglesensor, said motor torque and said motor angular speed are out ofapproximate value ranges of respective characteristic nominal values,said static characteristic learning is performed.
 11. The electric powersteering apparatus according to claim 8, wherein, when initial valuesbased on an actual measuring angle of said first angle sensor, an actualmeasuring angle of said second angle sensor, said motor torque and saidmotor angular speed are out of approximate value ranges of respectivecharacteristic nominal values, said static characteristic learning isperformed.
 12. The electric power steering apparatus according to claim1, wherein said learning is performed by a static characteristiclearning when a handle is a steering holding or a slow steering with apredetermined speed or less, and a dynamic characteristic learning and adelay characteristic learning when said handle is steered in a highspeed with a predetermined speed or more.
 13. The electric powersteering apparatus according to claim 2, wherein said learning isperformed by a static characteristic learning when a handle is asteering holding or a slow steering with a predetermined speed or less,and a dynamic characteristic learning and a delay characteristiclearning when said handle is steered in a high speed with apredetermined speed or more.
 14. The electric power steering apparatusaccording to claim 3, wherein said learning is performed by a staticcharacteristic learning when a handle is a steering holding or a slowsteering with a predetermined speed or less, and a dynamiccharacteristic learning and a delay characteristic learning when saidhandle is steered in a high speed with a predetermined speed or more.15. The electric power steering apparatus according to claim 12,wherein, when initial values based on an actual measuring angle of saidfirst angle sensor, an actual measuring angle of said second anglesensor, said motor torque and said motor angular speed are out ofapproximate value ranges of respective characteristic nominal values,said static characteristic learning, said dynamic characteristiclearning and said delay characteristic learning are performed.
 16. Theelectric power steering apparatus according to claim 13, wherein, wheninitial values based on an actual measuring angle of said first anglesensor, an actual measuring angle of said second angle sensor, saidmotor torque and said motor angular speed are out of approximate valueranges of respective characteristic nominal values, said staticcharacteristic learning, said dynamic characteristic learning and saiddelay characteristic learning are performed.
 17. The electric powersteering apparatus according to claim 14, wherein, when initial valuesbased on an actual measuring angle of said first angle sensor, an actualmeasuring angle of said second angle sensor, said motor torque and saidmotor angular speed are out of approximate value ranges of respectivecharacteristic nominal values, said static characteristic learning, saiddynamic characteristic learning and said delay characteristic learningare performed.
 18. The electric power steering apparatus according toclaim 1, wherein said steering shaft angle is a pinion-side angle to atorsion bar of said steering shaft.
 19. The electric power steeringapparatus according to claim 2, wherein said comparing is performed byjudging whether deviations between an estimating angle of said steeringshaft angle and an actual measuring angle of said first angle sensor,and between an estimating angle of said motor shaft and an actualmeasuring angle of said second angle sensor are within a tolerance rangeor not, and when an operation which said deviations are out of a rangeof said tolerance iterates predetermined times, a failure of saidsteering system or a sensor system is judged.
 20. An electric powersteering apparatus that a motor to assist-control a steering system of avehicle is connected to a steering shaft via a reduction mechanism, andcomprises a first angle sensor to detect a steering shaft angle of apinion side of said steering shaft, a second angle sensor to detect amotor shaft angle of said motor and a current detecting section todetect a motor current of said motor, comprising: a nonlinear logicalsection of nonlinear elements to calculate compensation value maps byiteratively learning characteristics of said nonlinear elementsincluding said reduction mechanism, by means of a motor torque based onsaid motor current, said steering shaft angle and a motor angular speedbased on said motor shaft angle; a steering shaft angle estimatingsection to estimate a steering shaft estimating angle by using saidcompensation value maps and said motor shaft angle; and a motor shaftangle estimating section to estimate a motor shaft estimating angle byusing said compensation value maps and said steering shaft angle. 21.The electric power steering apparatus according to claim 20, whereinsaid nonlinear logical section of said nonlinear elements comprises astatic characteristic compensating section to said motor torque and adynamic characteristic compensating section to said motor angular speed.22. The electric power steering apparatus according to claim 21, whereinsaid static characteristic compensating section outputs a firstcompensation value, said dynamic characteristic compensating sectionoutputs a second compensation value and an angle compensation value isgenerated by adding said second compensation value to said firstcompensation value.
 23. The electric power steering apparatus accordingto claim 21, wherein said static characteristic compensating sectioncomprises a low pass filter (LPF) to input said motor torque and anonlinear element static characteristic map to input a noise-removedmotor torque from said LPF.
 24. The electric power steering apparatusaccording to claim 22, wherein said static characteristic compensatingsection comprises a low pass filter (LPF) to input said motor torque anda nonlinear element static characteristic map to input a noise-removedmotor torque from said LPF.
 25. The electric power steering apparatusaccording to claim 21, wherein said dynamic characteristic compensatingsection comprises a nonlinear element dynamic characteristic map toinput said motor angular speed, and a nonlinear element delaycharacteristic map to input said noise-removed motor torque and a firstcompensation value from said dynamic characteristic map.
 26. Theelectric power steering apparatus according to claim 22, wherein saiddynamic characteristic compensating section comprises a nonlinearelement dynamic characteristic map to input said motor angular speed,and a nonlinear element delay characteristic map to input saidnoise-removed motor torque and a first compensation value from saiddynamic characteristic map.
 27. The electric power steering apparatusaccording to claim 23, wherein said dynamic characteristic compensatingsection comprises a nonlinear element dynamic characteristic map toinput said motor angular speed, and a nonlinear element delaycharacteristic map to input said noise-removed motor torque and a firstcompensation value from said dynamic characteristic map.
 28. Theelectric power steering apparatus according to claim 24, wherein saiddynamic characteristic compensating section comprises a nonlinearelement dynamic characteristic map to input said motor angular speed,and a nonlinear element delay characteristic map to input saidnoise-removed motor torque and a first compensation value from saiddynamic characteristic map.
 29. The electric power steering apparatusaccording to claim 23, wherein said nonlinear element staticcharacteristic map comprises a static characteristic learning judgingsection to output a first learning judging signal based on said motorangular speed, and a static characteristic learning logical section toinput said first learning judging signal, said motor torque, saidsteering shaft angle and said motor shaft angle.
 30. The electric powersteering apparatus according to claim 24, wherein said nonlinear elementstatic characteristic map comprises a static characteristic learningjudging section to output a first learning judging signal based on saidmotor angular speed, and a static characteristic learning logicalsection to input said first learning judging signal, said motor torque,said steering shaft angle and said motor shaft angle.
 31. The electricpower steering apparatus according to claim 29, wherein said staticcharacteristic learning logical section comprises a first additionaveraging section to input said motor torque, a second additionaveraging section to input a deviation between said steering shaft angleand said motor shaft angle, and a nonlinear element staticcharacteristic map creating section to input a first addition averagingvalue from said first addition averaging section and a second additionaveraging value from said second addition averaging section.
 32. Theelectric power steering apparatus according to claim 30, wherein saidstatic characteristic learning logical section comprises a firstaddition averaging section to input said motor torque, a second additionaveraging section to input a deviation between said steering shaft angleand said motor shaft angle, and a nonlinear element staticcharacteristic map creating section to input a first addition averagingvalue from said first addition averaging section and a second additionaveraging value from said second addition averaging section.
 33. Theelectric power steering apparatus according to claim 25, wherein saidnonlinear element dynamic characteristic map comprises said LPF, adynamic characteristic learning judging section to output a secondlearning judging signal based on a motor angular acceleration and amotor torque from said LPF, and a dynamic characteristic learninglogical section to input said second learning judging signal, a motorangular speed, said steering shaft angle and said motor shaft angle. 34.The electric power steering apparatus according to claim 33, whereinsaid dynamic characteristic learning logical section comprises saidnonlinear element static characteristic map, a third addition averagingsection to input said motor angular speed, a fourth addition averagingsection to input a deviation between an added value, which is added saidmotor shaft angle to a third compensation value from said staticcharacteristic map, and said steering shaft angle, and a nonlinearelement dynamic characteristic map creating section to input a thirdaddition averaging value from said third addition averaging section anda fourth addition averaging value from said fourth addition averagingsection.
 35. The electric power steering apparatus according to claim25, wherein said nonlinear element delay characteristic map comprises adelay characteristic learning judging section to output a third learningjudging signal based on said noise-removed motor torque, and a delaycharacteristic learning logical section to input said third learningjudging signal, said motor torque, said motor angular speed, saidsteering shaft angle and said motor shaft angle.
 36. The electric powersteering apparatus according to claim 35, wherein said delaycharacteristic learning logical section comprises said staticcharacteristic map, a fifth addition averaging section to input saidmotor torque, a multi delay section to said motor angular speed, a crosscorrelation section to input multi delay motor angular speeds from saidmulti delay section, and a deviation between an added value, which isadded said motor shaft angle to a third compensation value, and saidsteering shaft angle, and a nonlinear element delay characteristic mapcreating section to input a fifth addition averaging value from saidfifth addition averaging section and a cross correlation value from saidcross correlation section.
 37. The electric power steering apparatusaccording to claim 36, wherein said multi delay section comprises pluraldelay devices, and outputs multi delay motor angular speeds which haverespective predetermined delay times.
 38. The electric power steeringapparatus according to claim 37, wherein said cross correlation sectioncalculates correlation functions of said multi delay motor angularspeeds by using said steering shaft angle as a reference, and a delaytime whose correlation is largest in said multi delay motor angularspeeds is reflected to said delay characteristic map creating section.39. The electric power steering apparatus according to claim 20, whereinsaid learning is performed such that errors between estimating anglesand actual measuring angles are within a tolerance range, and when anoperation which said errors are out of a range of said toleranceiterates predetermined times, a failure of said steering system or asensor system is judged.
 40. The electric power steering apparatusaccording to claim 21, wherein said learning is performed such thaterrors between estimating angles and actual measuring angles are withina tolerance range, and when an operation which said errors are out of arange of said tolerance iterates predetermined times, a failure of saidsteering system or a sensor system is judged.
 41. The electric powersteering apparatus according to claim 22, wherein said learning isperformed such that errors between estimating angles and actualmeasuring angles are within a tolerance range, and when an operationwhich said errors are out of a range of said tolerance iteratespredetermined times, a failure of said steering system or a sensorsystem is judged.